Long term evolution (“LTE”) of the Third Generation Partnership Project (“3GPP”), also referred to as 3GPP LTE, refers to research and development involving the 3GPP LTE Release 8 and beyond, which is the name generally used to describe an ongoing effort across the industry aimed at identifying technologies and capabilities that can improve systems such as the universal mobile telecommunication system (“UMTS”). The notation “LTE-A” is generally used in the industry to refer to further advancements in LTE. The goals of this broadly based project include improving communication efficiency, lowering costs, improving services, making use of new spectrum opportunities, and achieving better integration with other open standards.
The evolved universal terrestrial radio access network (“E-UTRAN”) in 3GPP includes base stations providing user plane (including packet data convergence protocol/radio link control/media access control/physical (“PDCP/RLC/MAC/PHY”) sublayers) and control plane (including a radio resource control (“RRC”) sublayer) protocol terminations towards wireless communication devices such as cellular telephones. A wireless communication device or terminal is generally known as user equipment (also referred to as “UE”). A base station is an entity of a communication network often referred to as a Node B or an NB. Particularly in the E-UTRAN, an “evolved” base station is referred to as an eNodeB or an eNB. For details about the overall architecture of the E-UTRAN, see 3GPP Technical Specification (“TS”) 36.300 v8.7.0 (2008-12), which is incorporated herein by reference. For details of the communication or radio resource control management, see 3GPP TS 25.331 v.9.1.0 (2009-12) and 3GPP TS 36.331 v.9.1.0 (2009-12), which are incorporated herein by reference.
As wireless radio communication systems such as cellular telephone, satellite, and microwave communication systems become widely deployed and continue to attract a growing number of users, there is a pressing need to accommodate a large and variable amount of communication traffic with a minimal amount of processing resources, particularly in a mobile transceiver in wireless communication devices powered by a small battery. The increased quantity of data is a consequence of wireless communication devices transmitting video information and surfing the Internet, as well as performing ordinary voice communications.
One bottleneck in such communication systems is the need to process a large amount of data received at one end of a digital communication channel to detect a noisy signal transmitted substantially simultaneously by a plurality of transmit antennas, and which may be received substantially simultaneously by a plurality of receive antennas. Such communication channels that employ multiple antennas at either end are generally referred to as multi-input, multi-output (“MIMO”) communication channels.
Optimum soft MIMO wireless channel detection is conventionally based on Log-Maximum A Posteriori Probability (“Log-MAP”) detection, which is too computationally intensive to be implemented in a practical MIMO receiver (or transceiver), because the Log-MAP procedure requires calculating a log-sum of QM/2 exponential terms, wherein Q is the constellation size (i.e., the number of possible symbols of a modulation alphabet of a transmitted signal), and M is the number of transmit antennas. A brute-force implementation of an optimum Log-MAP procedure consumes enormous computing power, which makes it impractical to be employed in multiple antenna systems with higher-order modulation schemes. In practice, the Log-MAP procedure is often approximated by the Max-Log-MAP procedure to reduce computational complexity. The sub-optimal Max-Log-MAP approximation to the Log-MAP procedure, however, has a significant performance loss compared to the optimal Log-MAP procedure and, thus, there remains a significant performance gap between the sub-optimum Max-Log-MAP approximation and the optimal Log-MAP procedure. Existing MIMO detection implementations are based on the sub-optimal Max-Log-MAP approximation, which limits their error performance. Additionally, existing MIMO detection implementations are essentially independent processes that limit decoding performance.
Therefore, there is a need to develop a reduced-complexity replacement for the Log-MAP procedure for detection in a high-performance communication device including improved decoding processes that avoids the deficiencies of current communication systems.